class: center, middle, inverse, title-slide .title[ # Application of WHAM to Black Sea Bass ] .subtitle[ ## Black sea bass Research Track ] .author[ ### Tim Miller, Kierten Curti, Alex Hansell ] --- layout: true .footnote[U.S. Department of Commerce | National Oceanic and Atmospheric Administration | National Marine Fisheries Service] <style type="text/css"> code.cpp{ font-size: 14px; } code.r{ font-size: 14px; } </style> <style type="text/css"> pre { max-height: 400px; max-width: 800px; overflow-y: auto; } </style> --- #Outline <br> * Description of proposed base model * Diagnostics * Results * Path to the base model * Initial bridge building * Progression of model development --- layout: false class: middle, center # Description of proposed base model --- layout: true .footnote[U.S. Department of Commerce | National Oceanic and Atmospheric Administration | National Marine Fisheries Service] --- # General attributes * 2 regions * North * South * 2 stock components: * North * South * Model years: 1989 - 2021 * Ages: 1-8+ * 2 Environmental processes (1959-2022) * Bottom temperature in North * Bottom temperature in South * Natural mortality = 0.4 all ages, components, regions --- #Movement configuration: <img src="data:image/png;base64,#plots/migration_diagram_1.jpg" width="80%" style="display: block; margin: auto;" /> * All Jan 1 recruitment for a given stock component only in respective regions * North fish can only move from south to north in first 5 seasons * North fish can only move from north to south in last 4 seasons * Any remaining North fish that are in the south move back to their spawning region at end of season 5 * All North fish remain in North spawning region until end of season 7 * Spawning season is only time when whole North population is in spawning region * South population stays in South. --- #Movement configuration: * The monthly/seasonal movement matrix after spawning: `$$\mathbf{p}_{1} = \begin{bmatrix} 1-p_1 & p_1 \\ 0 & 1 \\ \end{bmatrix}$$` * Before spawning: `$$\mathbf{p}_{2} = \begin{bmatrix} 1 & 0 \\ p_2 & 1-p_2 \\ \end{bmatrix}$$` --- #Movement rates from Stock Synthesis * The Stock Synthesis model has 2 seasons (6 months each) * proportion `\(P_1\)` of the northern component moves to the south in one season and some proportion `\(P_2\)` move back to the south in the second season. * The movement matrices for each season are `$$\mathbf{P}_{1} = \begin{bmatrix} 1-P_1 & P_1 \\ 0 & 1 \\ \end{bmatrix}$$` and `$$\mathbf{P}_{2} = \begin{bmatrix} 1 & 0 \\ P_2 & 1-P_2 \\ \end{bmatrix}$$` --- #Transforming to shorter seasons in WHAM * Approximate the monthly movement matrices as the roots of `\(\mathbf{P}_1\)` and `\(\mathbf{P}_2\)` defined by the number of months of movement for each season (5 and 4, respectively): * Given the proportion parameter, the eigen decomposition of the matrices can be used to define the roots `$$\mathbf{P}_1^{\frac{1}{5}} = \mathbf{V}_1 \mathbf{D}_1^{\frac{1}{5}} \mathbf{V}_1^{-1}$$` `$$\mathbf{P}_2^{\frac{1}{4}} = \mathbf{V}_2 \mathbf{D}_2^{\frac{1}{4}} \mathbf{V}_2^{-1}$$` where `\(\mathbf{V}_i\)` and `\(\mathbf{D}_i\)` are the matrix of eigenvectors (columnwise) and the diagonal matrix of corresponding eigenvalues of `\(\mathbf{P}_i\)` for parameter `\(P_i\)`. --- #Parameterizing the prior distribution * The actual SS parameter estimates `\(x_1=-1.44\)` and `\(x_2=1.94\)` are transformations of `\(P_1=0.11\)` and `\(P_2=0.78\)` such that `$$P_i = \frac{1}{1 + 2e^{-x_i}}$$` * Multi-WHAM uses an additive logit transformation (simply a logit transformation when there are only two regions): `$$p_i = \frac{1}{1+e^{-y_i}}$$` --- #Parameterizing the movement prior distributions Used a parametric bootstrap approach: * Simulate 1000 values from a normal distribution with mean and standard deviation defined by the SS parameter estimate and standard error `\(\tilde x_i \sim N(x_i, SE(x_i))\)`. * For each simulated value * construct `\(\mathbf{P}_i\)`, * take the appropriate root, * calculated inverse logit for `\(\tilde y_i\)`. * calculate the mean and SD of the simulated values `\(\tilde y_i\)`. * mean values did not differ meaningfully from the transformation of the original estimates: `\(y_1 = -3.79\)` and `\(y_2 = -0.79\)` * SD was approximately 0.2 for both parameters. * distributions for random effects defining the movement parameters configured using the mean and SD from the bootstraps. --- #Initial abundance at age * With the movement configuration, northern origin fish (ages 2+) can occur in the southern region on January 1. * Estimating initial numbers at age as separate parameters can be challenging even in single-stock models. * To avoid difficulties, we used the equilibrium assumption described previously. * Two parameters are estimated for each regional stock component: an initial recruitment and an equilibrium full F across all fleets. --- #Recruitment and Survival/movement transitions * 2DAR1 (age and year) correlated random effects for both the northern and southern components. * Variance and correlation parameters are different for the northern and southern components. * Northern component: * abundance at age 1 on January 1 (recruitment) is only allowed in the northern region, * older individuals may occur in either region on Jan 1 (based on movement description) * survival random effects will occur for abundances at age in both regions. * Base model assumes very small variance for the transitions in the southern region (approximately SCAA) * 2DAR1 models with estimated variance for this region would not converge (correlation could not be estimated). --- # Observations * Aggregate catch: 2 fleets in each region: * Commercial (1989-2021) * Recreational (1989-2021) * Aggregate indices: 2 in each region: * Spring VAST (1989-2021) * Recreational CPA (1989-2021) * Age composition for all fleets and indices (1989-2021) * Model-based Bottom temperature observation in each region (1959-2022) --- # Selectivity `$$\\[12pt]$$` <table> <thead> <tr> <th style="text-align:left;"> Data component </th> <th style="text-align:left;"> Mean Selectivity model </th> <th style="text-align:left;"> Random effects configuration </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> North Commercial </td> <td style="text-align:left;"> age-specific (flat-topped at ages > 3) </td> <td style="text-align:left;"> 2D-AR1 (age and year) </td> </tr> <tr> <td style="text-align:left;"> North Recreational </td> <td style="text-align:left;"> age-specific (flat-topped at ages > 6) </td> <td style="text-align:left;"> 2D-AR1 (age and year) </td> </tr> <tr> <td style="text-align:left;"> South Commercial </td> <td style="text-align:left;"> logistic </td> <td style="text-align:left;"> None </td> </tr> <tr> <td style="text-align:left;"> South Recreational </td> <td style="text-align:left;"> logistic </td> <td style="text-align:left;"> None </td> </tr> <tr> <td style="text-align:left;"> North Recreational CPA </td> <td style="text-align:left;"> age-specific (flat-topped at ages > 1) </td> <td style="text-align:left;"> AR1 (year) </td> </tr> <tr> <td style="text-align:left;"> North VAST </td> <td style="text-align:left;"> age-specific (flat-topped at ages > 4) </td> <td style="text-align:left;"> 2D-AR1 (age and year) </td> </tr> <tr> <td style="text-align:left;"> South Recreational CPA </td> <td style="text-align:left;"> age-specific (flat-topped at ages > 2) </td> <td style="text-align:left;"> None </td> </tr> <tr> <td style="text-align:left;"> South VAST </td> <td style="text-align:left;"> age-specific (flat-topped at ages > 1) </td> <td style="text-align:left;"> None </td> </tr> </tbody> </table> --- # Age composition models `$$\\[12pt]$$` <table> <thead> <tr> <th style="text-align:left;"> Data component </th> <th style="text-align:left;"> Age Composition Likelihood </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> North Commercial </td> <td style="text-align:left;"> Dirichlet-Multinomial </td> </tr> <tr> <td style="text-align:left;"> North Recreational </td> <td style="text-align:left;"> Logistic-normal (0s as missing) </td> </tr> <tr> <td style="text-align:left;"> South Commercial </td> <td style="text-align:left;"> Logistic-normal (AR1, 0s as missing) </td> </tr> <tr> <td style="text-align:left;"> South Recreational </td> <td style="text-align:left;"> Logistic-normal (AR1, 0s as missing) </td> </tr> <tr> <td style="text-align:left;"> North Recreational CPA </td> <td style="text-align:left;"> Logistic-normal (0s as missing) </td> </tr> <tr> <td style="text-align:left;"> North VAST </td> <td style="text-align:left;"> Dirichlet-Multinomial </td> </tr> <tr> <td style="text-align:left;"> South Recreational CPA </td> <td style="text-align:left;"> Logistic-normal (AR1, 0s as missing) </td> </tr> <tr> <td style="text-align:left;"> South VAST </td> <td style="text-align:left;"> Logistic-normal (AR1, 0s as missing) </td> </tr> </tbody> </table> --- #Uncertainty in Rec CPA indices * CVs provided by analyses that generated Rec CPA indices were deemed implausibly small (CVs: 0.02 to 0.06). * In many early runs and proposed base model we estimated a scalar of the SD of the log-aggregate Rec CPA indices. * Estimates of the scalar were usually approximately 5 for the north and the south Rec CPA indices. * Estimation included in the proposed base model allow more realistic estimates of uncertainty in model output. --- #Bottom Temperature effects on recruitment * Included model-based bottom temperature observations in the BSB model. * Very small uncertainty in observations (SEs: 0.03 to 0.09). * State-space treatment: * Modeled latent covariate as AR1 process. `$$X_y \sim N\left(\mu_X(1-\rho_X) + \rho_X X_{y-1}, \sigma_X^2\right)$$` * Observations of the latent covariate: `$$x_y \sim N\left(X_{y}, \sigma_x^2\right)$$` * Fit models with and without effects on northern and southern recruitment `$$\log R_y = \mu_R + \beta X_y + \epsilon_y.$$` * Examined AIC for full model and all retrospective peels. * Might be worth exploring an alternative effect on `\(M\)` for age 1. * Would also affect reference points and projections differently. --- layout: false class: middle, center # Diagnostics for base model --- layout: true .footnote[U.S. Department of Commerce | National Oceanic and Atmospheric Administration | National Marine Fisheries Service] --- # Jitter analysis * Simulated 100 fixed effects parameter vectors from a normal distribution with mean equal to the optimized values and covariance equal to the hessian-based covariance matrix of the optimzed model. * All of the re-fits of the mdoel resulted in the same marginal negative log-likelihood. * The gradients at these optimized values were all satisfactory with maximum absolute values less the `\(10^{-9}\)`. --- #Jitter analysis <img src="data:image/png;base64,#bsbrt_peer_review_bsbapp_files/figure-html/unnamed-chunk-6-1.png" style="display: block; margin: auto;" /> --- #Self-test * Simulated new observations conditional on all random effects estimated in the proposed base model * Fitted the same model configuration each of the simulated data sets. * For 7 of the the simulated data sets the model failed to optimize. * The maximum absolute gradient was `\(<10^{-6}\)` for only 9 and `\(<10^{-4}\)` for 52 of the 93 successfully fitted models. * The poor convergence appeared to be attributable to the estimation of the scalar for the standard errors of the log-transformed northern Recreational CPA index * Estimates tended to 0 for nearly all of the fits ($<0.01$ for 83 fits). * Even across all fits including those with poor convergence, the SSB estimates appeared to be reliable. --- #Self-test <img src="data:image/png;base64,#bsbrt_peer_review_bsbapp_files/figure-html/unnamed-chunk-7-1.png" style="display: block; margin: auto;" /> --- #MASE * We fit 7 configurations of the model where the last 1 to 7 years of aggregate index and age composition observations were removed sequentially (peels). * Calculate the mean absolute scaled error (MASE) of the predictions at 1 to 5 years beyond the final year of index observations (horizons). * The mean absolute errors are scaled by the mean absolute errors of so-called naïve predictions using the aggregate index observation from the final year of each peel. * A MASE < 1 results when the mean absolute error is greater using the model than the naïve forecast. * For the proposed base model, predictions for 3 of the 4 surveys performed similarly to naïve predictions across all horizons, * BUT MASE scores were much greater than 1 for the northern region recreational CPA index at all horizons. * The large MASE values occur because the index has no trend and low variability over the years used for the calculation whereas the model predictions vary much more. --- #MASE <img src="data:image/png;base64,#../2023.RT.Runs/Run34/mase_WHAM_for_unnamed_stock.png" width="50%" style="display: block; margin: auto;" /> --- #MASE <img src="data:image/png;base64,#../2023.RT.Runs/Run34/predicted_vs_obs_WHAM_for_unnamed_stock.png" width="50%" style="display: block; margin: auto;" /> --- #One-step-ahead residuals * For composition observations and for aggregate observations in state-space models, Pearson residuals do not have the appropriate properties: * independent standard normal for a correctly specified model * Due to lack of independence of observations. * One-step-ahead (OSA) residuals have this property * However, understanding causes of mis-specification can be difficult. --- <!-- OSA North Comm--> #OSA: North commercial fleet .pull-left[ * no evidence of mis-specification aggregate catch. ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_catch_4panel_North_Commercial.png" width="100%" style="display: block; margin: auto;" /> ] --- #OSA: North commercial fleet * some indication of trends in residuals of some of the age composition observations early in the time series and for the the first age class. .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_paa_6panel_North_Commercial.png" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/Catch_age_comp_osa_resids_North_Commercial.png" width="90%" style="display: block; margin: auto;" /> ] --- <!-- OSA North Rec--> #OSA: North recreational fleet .pull-left[ * residuals appeared satisfactory. ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_catch_4panel_North_Recreational.png" width="100%" style="display: block; margin: auto;" /> ] --- #OSA: North recreational fleet * residuals showed some tendency of underdispersion .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_paa_6panel_North_Recreational.png" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/Catch_age_comp_osa_resids_North_Recreational.png" width="90%" style="display: block; margin: auto;" /> ] --- <!-- OSA North rec cpa --> #OSA: North Rec CPA index .pull-left[ * no signs of mis-specification ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_catch_4panel_North_REC_CPA.png" width="100%" style="display: block; margin: auto;" /> ] --- #OSA: North Rec CPA index * no signs of mis-specification .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_paa_6panel_North_REC_CPA.png" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/Catch_age_comp_osa_resids_North_REC_CPA.png" width="90%" style="display: block; margin: auto;" /> ] --- <!-- OSA North vast --> #OSA: North VAST index .pull-left[ * some evidence of tendency toward negative residuals ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_catch_4panel_North_VAST_Spring.png" width="100%" style="display: block; margin: auto;" /> ] --- #OSA: North VAST index * some indication of positive residuals, particularly at age 1 .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_paa_6panel_North_VAST_Spring.png" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/Catch_age_comp_osa_resids_North_VAST_Spring.png" width="90%" style="display: block; margin: auto;" /> ] --- <!-- OSA South Comm--> #OSA: South commercial fleet .pull-left[ * residuals tended to be negative ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_catch_4panel_South_Commercial.png" width="100%" style="display: block; margin: auto;" /> ] --- #OSA: South commercial fleet * no evidence of mis-specification .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_paa_6panel_South_Commercial.png" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/Catch_age_comp_osa_resids_South_Commercial.png" width="90%" style="display: block; margin: auto;" /> ] --- <!-- OSA South Rec--> #OSA: South recreational fleet .pull-left[ * residuals were under-dispersed but no trends. ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_catch_4panel_South_Recreational.png" width="100%" style="display: block; margin: auto;" /> ] --- #OSA: South recreational fleet * somewhat underdispersed, but no trends .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_paa_6panel_South_Recreational.png" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/Catch_age_comp_osa_resids_South_Recreational.png" width="90%" style="display: block; margin: auto;" /> ] --- <!-- OSA South rec cpa --> #OSA: South Rec CPA index .pull-left[ * somewhat under-dispersed ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_catch_4panel_South_REC_CPA.png" width="100%" style="display: block; margin: auto;" /> ] --- #OSA: South Rec CPA index * a few large negative residuals at older ages, but otherwise no apparent trends .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_paa_6panel_South_REC_CPA.png" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/Catch_age_comp_osa_resids_South_REC_CPA.png" width="90%" style="display: block; margin: auto;" /> ] --- <!-- OSA South vast --> #OSA: South VAST index .pull-left[ * somewhat over-dispersed ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_catch_4panel_South_VAST_Spring.png" width="100%" style="display: block; margin: auto;" /> ] --- #OSA: South VAST index * some trend with observed proportion and age .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/OSA_resid_paa_6panel_South_VAST_Spring.png" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/diagnostics/Catch_age_comp_osa_resids_South_VAST_Spring.png" width="90%" style="display: block; margin: auto;" /> ] --- #Retrospective patterns * Seven peels of the base model. * Strong retrospective patterns in the most recent management track assessment for the northern component of the stock, do not occur in base model. --- #Retrospective patterns: North SSB .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/retro/BSB_North_SSB_retro.png" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/retro/BSB_North_SSB_retro_relative.png" width="100%" style="display: block; margin: auto;" /> ] --- #Retrospective patterns: North F (average of ages 6-7) .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/retro/North_Fbar_retro.png" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/retro/North_Fbar_retro_relative.png" width="100%" style="display: block; margin: auto;" /> ] --- #Retrospective patterns: South SSB .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/retro/BSB_South_SSB_retro.png" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/retro/BSB_South_SSB_retro_relative.png" width="100%" style="display: block; margin: auto;" /> ] --- #Retrospective patterns: South F (average of ages 6-7) .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/retro/South_Fbar_retro.png" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/retro/South_Fbar_retro_relative.png" width="100%" style="display: block; margin: auto;" /> ] --- layout: false class: middle, center #Results --- layout: true .footnote[U.S. Department of Commerce | National Oceanic and Atmospheric Administration | National Marine Fisheries Service] --- # SSB, R: North <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/SSB_Rec_time_BSB_North.png" width="50%" style="display: block; margin: auto;" /> --- # SSB, R: South <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/SSB_Rec_time_BSB_South.png" width="50%" style="display: block; margin: auto;" /> --- # SSB, R: Total <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/SSB_Rec_time_total.png" width="50%" style="display: block; margin: auto;" /> --- # SSB, F <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/SSB_F_trend.png" width="50%" style="display: block; margin: auto;" /> --- #F by fleet <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/F_byfleet.png" width="50%" style="display: block; margin: auto;" /> --- # Bottom temperature .pull-left[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/Ecov_1_North_BT.png" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/Ecov_2_South_BT.png" width="100%" style="display: block; margin: auto;" /> ] --- # Northern recruitment and bottom temperature <img src="data:image/png;base64,#bsbrt_peer_review_bsbapp_files/figure-html/unnamed-chunk-26-1.png" width="50%" style="display: block; margin: auto;" /> --- # NAA random effects deviations <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/NAA_dev_tile.png" width="100%" style="display: block; margin: auto;" /> --- #Selectivity <img src="data:image/png;base64,#../2023.RT.Runs/Run34/plots_png/results/SelAA_tile.png" width="50%" style="display: block; margin: auto;" /> --- # Prior and posterior movement rates <!-- --> --- layout: false class: middle, center # Path to the base model --- layout: true .footnote[U.S. Department of Commerce | National Oceanic and Atmospheric Administration | National Marine Fisheries Service] --- #Comparing ASAP with standard WHAM and Multi-WHAM .pull-left[ * Based on data used in last management track * Time series of observations not updated * Use previous fleet definitions (Trawl and Non-Trawl). * Compared * Management track ASAP * Separate fits in standard WHAM * Simultaneous (separate) fits in Multi-WHAM ] .pull-right[ <img src="data:image/png;base64,#plots/compare_one_stock_models.png" width="100%" style="display: block; margin: auto;" /> ] --- # Bridge runs Separate fits for north and south regions * 1: Turn off all indices but NEFSC Spring BTS and Rec CPA * 2: Update fishery catches, fishing fleets and catch WAA estimates to comm/rec fleets * 3: Update Spring BTS and Rec CPA * 4: Add 2020-2021 * 5: Update maturity * 6: Add NEAMAP * 7: Update remaining spring state indices (added VAST as well but didn’t turn them on) * <span style='color: #B3B3B3;'>8: Rec CPA and both spring and fall VAST</span> * 9: Rec CPA and VAST spring only (also a combined stock run that matches the single stock results. This combined run will be used for later runs.) --- # Bridge runs Separate fits for north and south regions * <span style='color: red;'>1: Turn off all indices but NEFSC Spring BTS and Rec CPA</span> * <span style='color: red;'>2: Update fishery catches, fishing fleets and catch WAA estimates to comm/rec fleets</span> * <span style='color: red;'>3: Update Spring BTS and Rec CPA</span> * 4: Add 2020-2021 * 5: Update maturity * 6: Add NEAMAP * 7: Update remaining spring state indices (added VAST as well but didn’t turn them on) * <span style='color: #B3B3B3;'>8: Rec CPA and both spring and fall VAST</span> * 9: Rec CPA and VAST spring only (also a combined stock run that matches the single stock results. This combined run will be used for later runs.) --- # Bridge runs 1, 2, and 3 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs1-3.north.F.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs1-3.south.F.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs 1, 2, and 3 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs1-3.north.SSB.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs1-3.south.SSB.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs 1, 2, and 3 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs1-3.north.Rect.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs1-3.south.Rect.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs Separate fits for north and south regions * 1: Turn off all indices but NEFSC Spring BTS and Rec CPA * 2: Update fishery catches, fishing fleets and catch WAA estimates to comm/rec fleets * <span style='color: red;'>3: Update Spring BTS and Rec CPA</span> * <span style='color: red;'>4: Add 2020-2021</span> * 5: Update maturity * 6: Add NEAMAP * 7: Update remaining spring state indices (added VAST as well but didn’t turn them on) * 8: Rec CPA and both spring and fall VAST * 9: Rec CPA and VAST spring only (also a combined stock run that matches the single stock results. This combined run will be used for later runs.) --- # Bridge runs 3 and 4 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs3-4.north.F.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs3-4.south.F.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs 3 and 4 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs3-4.north.SSB.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs3-4.south.SSB.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs 3 and 4 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs3-4.north.Rect.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs3-4.south.Rect.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs Separate fits for north and south regions * 1: Turn off all indices but NEFSC Spring BTS and Rec CPA * 2: Update fishery catches, fishing fleets and catch WAA estimates to comm/rec fleets * 3: Update Spring BTS and Rec CPA * <span style='color: red;'>4: Add 2020-2021</span> * <span style='color: red;'>5: Update maturity</span> * <span style='color: red;'>6: Add NEAMAP</span> * <span style='color: red;'>7: Update remaining spring state indices (added VAST as well but didn’t turn them on)</span> * 8: Rec CPA and both spring and fall VAST * 9: Rec CPA and VAST spring only (also a combined stock run that matches the single stock results. This combined run will be used for later runs.) --- # Bridge runs 4, 5, 6, and 7 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs4-7.north.F.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs4-7.south.F.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs 4, 5, 6, and 7 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs4-7.north.SSB.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs4-7.south.SSB.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs 4, 5, 6, and 7 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs4-7.north.Rect.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs4-7.south.Rect.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs Separate fits for north and south regions * 1: Turn off all indices but NEFSC Spring BTS and Rec CPA * 2: Update fishery catches, fishing fleets and catch WAA estimates to comm/rec fleets * 3: Update Spring BTS and Rec CPA * 4: Add 2020-2021 * 5: Update maturity * 6: Add NEAMAP * <span style='color: red;'>7: Update remaining spring state indices (added VAST as well but didn’t turn them on)</span> * <span style='color: red;'>8: Rec CPA and both spring and fall VAST</span> * <span style='color: red;'>9: Rec CPA and VAST spring only (also a combined stock run that matches the single stock results. This combined run will be used for later runs.)</span> --- # Bridge runs 7, 8, and 9 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs7-9.north.F.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs7-9.south.F.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs 7, 8, and 9 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs7-9.north.SSB.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs7-9.south.SSB.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Bridge runs 7, 8, and 9 .pull-left[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs7-9.north.Rect.png" alt="North" width="100%" /> <p class="caption">North</p> </div> ] .pull-right[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#../Bridge.Runs/Comparison_Figures/Runs7-9.south.Rect.png" alt="South" width="100%" /> <p class="caption">South</p> </div> ] --- # Early research track runs (1-18) * See table in the working paper * Used updated and new observations * Included surveys as either separate indices or combined in fall and spring VAST indices (other than the recreational CPA) * Explored alternative selectivity and age comp likelihood assumptions: reduce patterns in age comp OSA residuals. * WG determined to use aggregate VAST indices that account for changes in catchability. * **However, age comp for fall VAST and NEAMAP were incorrectly calculated** * **Runs 19+ only used the spring VAST and Rec CPA indices.** --- # Middle runs (19-27) * Issues with patterns in OSA residuals remained * Sometimes large retrospective patterns for the northern component. * After Run 27, we used separate models for north and south to more efficiently explore alternative assumptions about selectivity and age composition likelihoods * **These analyses resulted in assumptions that remained the same across all later runs** * **Different age composition model assumptions for some of the fleets and indices.** * **Use of selectivity random effects for the northern fleets and indices.** --- # Late runs (28-34) * **Corrected some previous assumptions about movement.** * Rates from SS were not corrected for differences in seasonal time steps * Rates of movement for northern component to and from north were both occurring over all seasonal intervals outside of spawning * Incorrect movement rate from south to north. * Runs 30+ assume negligible variance of survival random effects for northern origin fish occurring in the south on Jan 1. * Allows yearly AR1 correlation to be estimated. * Runs that did not converge well: * Run 31 assumed temporal random effects on the movement rate from the north to the south * Run 32 assumed a Beverton-Holt stock recruit relationships for both the north and south components * Run 33 investigated bottom temperature effects on recruitment and we found evidence of an effect on northern recruitment. * Run 34 relaxed estimated scalar for uncertainty in the Recreational CPA indices * **After these runs we discovered a coding error in constructing initial numbers at age under an equilibrium assumption. * Small changes in results Run 34: absolute differences in annual SSB estimates were less than 7%. * **Refit Runs 30, 33, and 34 with the corrected model** * None of the choices among these models would have changed. --- #Evidence for bottom temperature effects on recruitment `$$\\[12pt]$$` .pull-left[ <table> <thead> <tr> <th style="text-align:left;"> </th> <th style="text-align:right;"> AIC </th> <th style="text-align:right;"> North `\(\widehat{\sigma}_R\)` </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> No effect </td> <td style="text-align:right;"> -1551.79 </td> <td style="text-align:right;"> 0.92 </td> </tr> <tr> <td style="text-align:left;"> North temperature effect only </td> <td style="text-align:right;"> -1562.14 </td> <td style="text-align:right;"> 0.74 </td> </tr> <tr> <td style="text-align:left;"> Both temperature effects </td> <td style="text-align:right;"> -1561.61 </td> <td style="text-align:right;"> 0.74 </td> </tr> </tbody> </table> ] .pull-right[ <table> <thead> <tr> <th style="text-align:right;"> Peel </th> <th style="text-align:right;"> No effect </th> <th style="text-align:right;"> North only </th> <th style="text-align:right;"> North and South </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> 0 </td> <td style="text-align:right;"> 10.35 </td> <td style="text-align:right;"> 0.00 </td> <td style="text-align:right;"> 0.53 </td> </tr> <tr> <td style="text-align:right;"> 1 </td> <td style="text-align:right;"> 10.17 </td> <td style="text-align:right;"> 0.00 </td> <td style="text-align:right;"> 0.38 </td> </tr> <tr> <td style="text-align:right;"> 2 </td> <td style="text-align:right;"> 9.22 </td> <td style="text-align:right;"> 0.00 </td> <td style="text-align:right;"> 0.65 </td> </tr> <tr> <td style="text-align:right;"> 3 </td> <td style="text-align:right;"> 9.29 </td> <td style="text-align:right;"> 0.00 </td> <td style="text-align:right;"> 0.56 </td> </tr> <tr> <td style="text-align:right;"> 4 </td> <td style="text-align:right;"> 9.07 </td> <td style="text-align:right;"> 0.00 </td> <td style="text-align:right;"> 0.70 </td> </tr> <tr> <td style="text-align:right;"> 5 </td> <td style="text-align:right;"> 8.53 </td> <td style="text-align:right;"> 0.00 </td> <td style="text-align:right;"> 0.31 </td> </tr> <tr> <td style="text-align:right;"> 6 </td> <td style="text-align:right;"> 8.20 </td> <td style="text-align:right;"> 0.36 </td> <td style="text-align:right;"> 0.00 </td> </tr> <tr> <td style="text-align:right;"> 7 </td> <td style="text-align:right;"> 7.78 </td> <td style="text-align:right;"> 0.11 </td> <td style="text-align:right;"> 0.00 </td> </tr> </tbody> </table> ] --- # Comparison of Runs 30, 33 and 34 * Run 30: like run 34, but fixed Rec CPA CVs, no temperature effects on northern recruitment. * Run 33: like Run 34, but fixed Rec CPA CVs * The estimates of SSB, fully-selected fishing mortality, and recruitment were very similar for Runs 30, 33, and 34 --- # Comparison of Runs 30, 33 and 34: SSB <img src="data:image/png;base64,#bsbrt_peer_review_bsbapp_files/figure-html/SSB-compare-1.png" style="display: block; margin: auto;" /> --- # Comparison of Runs 30, 33 and 34: Fishing mortality <img src="data:image/png;base64,#bsbrt_peer_review_bsbapp_files/figure-html/F-compare-1.png" style="display: block; margin: auto;" /> --- # Comparison of Runs 30, 33 and 34: Recruitment <img src="data:image/png;base64,#bsbrt_peer_review_bsbapp_files/figure-html/unnamed-chunk-54-1.png" style="display: block; margin: auto;" /> --- #Expected recruitment with and without temperature effects <img src="data:image/png;base64,#bsbrt_peer_review_bsbapp_files/figure-html/unnamed-chunk-55-1.png" width="50%" style="display: block; margin: auto;" /> --- # Comparison of Runs 33 and 34 * Allowing the scalar for the standard error of the log-transformed Rec CPA indices to be estimated in the proposed base model results in a slight increase in uncertainty of spawning stock biomass and recruitment estimates. * fitted models with `\((\text{SE}_1)\)` and without `\((\text{SE}_2)\)` the scalar of the Recreational CPA index standard errors estimated. <img src="data:image/png;base64,#bsbrt_peer_review_bsbapp_files/figure-html/relative-se-ssb-R-1.png" style="display: block; margin: auto;" />